Edge Computing for Real Time Botnet Propagation Detection

Abstract

Continued growth and adoption of the Internet of Things (IoT) has greatly increased the number of dispersed resources within both corporate and private networks. IoT devices benefit the user by providing more local access to computation and observation compared to dedicated servers within a centralized data center. However, years of lax or nonexistent cybersecurity standards leave IoT devices as easy prey for hackers looking for easy targets. Further, IoT devices normally operate at the edge of the network, far from sophisticated cyberattack detection and network monitoring tools. When hacked, IoT can be used as a launching point to attack more sensitive targets or can be collected into a larger botnet. These botnets are frequently utilized for targeted Distributed Denial of Service (DDoS) attacks against service providers and servers, decreasing response time or overwhelming the system. In order to protect these vulnerable resources, we propose an edge computing system for detecting active threats against local IoT devices. Our system will utilize deep learning, specifically a Convolutional Neural Network (CNN) for detecting attacks. Incoming network traffic will be converted into an image before beings supplied to the CNN for classification. The network will be trained using the N-BaIoT dataset. Since the system is designed to operate at the edge of the network, it will run on the Jetson Nano for real-time attack detection.